Overview

Dataset statistics

Number of variables25
Number of observations8440
Missing cells94755
Missing cells (%)44.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory791.3 B

Variable types

Categorical12
Numeric10
Unsupported3

Alerts

ClaseVehiculo__c has constant value "99999" Constant
TipoVehiculo__c has constant value "99999" Constant
churn is highly correlated with n_prod_prev and 3 other fieldsHigh correlation
n_prod_prev is highly correlated with churn and 3 other fieldsHigh correlation
total_siniestros is highly correlated with churn and 3 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with churn and 3 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with churn and 3 other fieldsHigh correlation
Activos__c is highly correlated with AnnualRevenue and 1 other fieldsHigh correlation
AnnualRevenue is highly correlated with Activos__c and 1 other fieldsHigh correlation
EgresosAnuales__c is highly correlated with Activos__c and 1 other fieldsHigh correlation
churn is highly correlated with n_prod_prev and 2 other fieldsHigh correlation
n_prod_prev is highly correlated with churn and 2 other fieldsHigh correlation
total_siniestros is highly correlated with churn and 2 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with churn and 2 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with AnnualRevenue and 1 other fieldsHigh correlation
Activos__c is highly correlated with AnnualRevenue and 1 other fieldsHigh correlation
AnnualRevenue is highly correlated with anios_ultimo_siniestro and 2 other fieldsHigh correlation
EgresosAnuales__c is highly correlated with anios_ultimo_siniestro and 2 other fieldsHigh correlation
churn is highly correlated with n_prod_prev and 3 other fieldsHigh correlation
n_prod_prev is highly correlated with churn and 3 other fieldsHigh correlation
total_siniestros is highly correlated with churn and 3 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with churn and 3 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with churn and 3 other fieldsHigh correlation
AnnualRevenue is highly correlated with EgresosAnuales__cHigh correlation
EgresosAnuales__c is highly correlated with AnnualRevenueHigh correlation
CodigoTipoAsegurado__c is highly correlated with n_prod_prev and 2 other fieldsHigh correlation
PuntoVenta__c is highly correlated with tipo_ramo_name and 4 other fieldsHigh correlation
tipo_ramo_name is highly correlated with PuntoVenta__c and 5 other fieldsHigh correlation
tipo_prod_desc is highly correlated with tipo_ramo_name and 3 other fieldsHigh correlation
NumeroPoliza__c is highly correlated with PuntoVenta__c and 5 other fieldsHigh correlation
FechaInicioVigencia__ctrim is highly correlated with n_prod_prev and 2 other fieldsHigh correlation
churn is highly correlated with n_prod_prev and 2 other fieldsHigh correlation
n_prod_prev is highly correlated with CodigoTipoAsegurado__c and 8 other fieldsHigh correlation
total_siniestros is highly correlated with CodigoTipoAsegurado__c and 7 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with CodigoTipoAsegurado__c and 9 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with n_prod_prev and 3 other fieldsHigh correlation
Activos__c is highly correlated with anios_ultimo_siniestro and 4 other fieldsHigh correlation
AnnualRevenue is highly correlated with anios_ultimo_siniestro and 3 other fieldsHigh correlation
MontoAnual__c is highly correlated with FechaInicioVigencia__ctrim and 3 other fieldsHigh correlation
OtrosIngresos__c is highly correlated with Activos__c and 2 other fieldsHigh correlation
EgresosAnuales__c is highly correlated with anios_ultimo_siniestro and 3 other fieldsHigh correlation
EstadoCivil__pc is highly correlated with Genero__pcHigh correlation
Genero__pc is highly correlated with MontoAnual__c and 1 other fieldsHigh correlation
ciudad_name is highly correlated with total_pagado_smmlvHigh correlation
edad is highly correlated with MontoAnual__cHigh correlation
MarcaVehiculo__c has 8440 (100.0%) missing values Missing
MdeloVehiculo__c has 8440 (100.0%) missing values Missing
n_prod_prev has 6743 (79.9%) missing values Missing
total_siniestros has 6581 (78.0%) missing values Missing
total_pagado_smmlv has 6581 (78.0%) missing values Missing
anios_ultimo_siniestro has 6581 (78.0%) missing values Missing
Activos__c has 4282 (50.7%) missing values Missing
AnnualRevenue has 4282 (50.7%) missing values Missing
MontoAnual__c has 8433 (99.9%) missing values Missing
OtrosIngresos__c has 5105 (60.5%) missing values Missing
Profesion__pc has 8440 (100.0%) missing values Missing
EgresosAnuales__c has 4282 (50.7%) missing values Missing
EstadoCivil__pc has 4141 (49.1%) missing values Missing
Genero__pc has 4141 (49.1%) missing values Missing
ciudad_name has 4141 (49.1%) missing values Missing
edad has 4142 (49.1%) missing values Missing
OtrosIngresos__c is highly skewed (γ1 = 51.4657287) Skewed
MarcaVehiculo__c is an unsupported type, check if it needs cleaning or further analysis Unsupported
MdeloVehiculo__c is an unsupported type, check if it needs cleaning or further analysis Unsupported
Profesion__pc is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_pagado_smmlv has 194 (2.3%) zeros Zeros
OtrosIngresos__c has 3168 (37.5%) zeros Zeros

Reproduction

Analysis started2022-06-04 05:31:45.493506
Analysis finished2022-06-04 05:33:02.439508
Duration1 minute and 16.95 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

CodigoTipoAsegurado__c
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.2 KiB
1
8040 
4
 
291
3
 
58
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8440
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row4

Common Values

ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

Length

2022-06-04T00:33:02.487506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:02.561007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

Most occurring characters

ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8440
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common8440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII8440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18040
95.3%
4291
 
3.4%
358
 
0.7%
251
 
0.6%

PuntoVenta__c
Real number (ℝ≥0)

HIGH CORRELATION

Distinct175
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3388.13519
Minimum5
Maximum20001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:02.635006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile103
Q11000
median2466
Q33502
95-th percentile9721
Maximum20001
Range19996
Interquartile range (IQR)2502

Descriptive statistics

Standard deviation3360.801403
Coefficient of variation (CV)0.9919324982
Kurtosis-0.1232953451
Mean3388.13519
Median Absolute Deviation (MAD)1464
Skewness1.078266357
Sum28595861
Variance11294986.07
MonotonicityNot monotonic
2022-06-04T00:33:02.726511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33011007
 
11.9%
9721977
 
11.6%
404400
 
4.7%
1048396
 
4.7%
1503375
 
4.4%
103339
 
4.0%
1002311
 
3.7%
3502302
 
3.6%
301268
 
3.2%
1203198
 
2.3%
Other values (165)3867
45.8%
ValueCountFrequency (%)
5127
 
1.5%
83
 
< 0.1%
95
 
0.1%
1430
 
0.4%
1638
 
0.5%
2375
 
0.9%
2538
 
0.5%
2649
 
0.6%
1025
 
0.1%
103339
4.0%
ValueCountFrequency (%)
200016
 
0.1%
101116
 
0.1%
99776
 
0.1%
99711
 
< 0.1%
99433
 
< 0.1%
98202
 
< 0.1%
97234
 
< 0.1%
9721977
11.6%
971517
 
0.2%
97062
 
< 0.1%

tipo_ramo_name
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size596.6 KiB
automoviles
4754 
responsabilidad civil
3686 

Length

Max length21
Median length11
Mean length15.36729858
Min length11

Characters and Unicode

Total characters129700
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowresponsabilidad civil
2nd rowautomoviles
3rd rowautomoviles
4th rowautomoviles
5th rowresponsabilidad civil

Common Values

ValueCountFrequency (%)
automoviles4754
56.3%
responsabilidad civil3686
43.7%

Length

2022-06-04T00:33:02.808006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:02.873006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
automoviles4754
39.2%
responsabilidad3686
30.4%
civil3686
30.4%

Most occurring characters

ValueCountFrequency (%)
i19498
15.0%
o13194
10.2%
a12126
9.3%
s12126
9.3%
l12126
9.3%
e8440
 
6.5%
v8440
 
6.5%
d7372
 
5.7%
m4754
 
3.7%
u4754
 
3.7%
Other values (7)26870
20.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter126014
97.2%
Space Separator3686
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i19498
15.5%
o13194
10.5%
a12126
9.6%
s12126
9.6%
l12126
9.6%
e8440
 
6.7%
v8440
 
6.7%
d7372
 
5.9%
m4754
 
3.8%
u4754
 
3.8%
Other values (6)23184
18.4%
Space Separator
ValueCountFrequency (%)
3686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126014
97.2%
Common3686
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i19498
15.5%
o13194
10.5%
a12126
9.6%
s12126
9.6%
l12126
9.6%
e8440
 
6.7%
v8440
 
6.7%
d7372
 
5.9%
m4754
 
3.8%
u4754
 
3.8%
Other values (6)23184
18.4%
Common
ValueCountFrequency (%)
3686
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII129700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i19498
15.0%
o13194
10.2%
a12126
9.3%
s12126
9.3%
l12126
9.3%
e8440
 
6.5%
v8440
 
6.5%
d7372
 
5.7%
m4754
 
3.7%
u4754
 
3.7%
Other values (7)26870
20.7%

tipo_prod_desc
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size582.6 KiB
automoviles
4596 
extracontractual
2602 
profesionales medicos
697 
operadores portuarios
 
362
otras
 
183

Length

Max length21
Median length11
Mean length13.66611374
Min length5

Characters and Unicode

Total characters115342
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowextracontractual
2nd rowautomoviles
3rd rowautomoviles
4th rowautomoviles
5th rowextracontractual

Common Values

ValueCountFrequency (%)
automoviles4596
54.5%
extracontractual2602
30.8%
profesionales medicos697
 
8.3%
operadores portuarios362
 
4.3%
otras183
 
2.2%

Length

2022-06-04T00:33:02.936007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.013005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
automoviles4596
48.4%
extracontractual2602
27.4%
profesionales697
 
7.3%
medicos697
 
7.3%
operadores362
 
3.8%
portuarios362
 
3.8%
otras183
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o15516
13.5%
a14006
12.1%
t12947
11.2%
e10013
8.7%
l7895
 
6.8%
s7594
 
6.6%
u7560
 
6.6%
r7532
 
6.5%
i6352
 
5.5%
c5901
 
5.1%
Other values (8)20026
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter114283
99.1%
Space Separator1059
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o15516
13.6%
a14006
12.3%
t12947
11.3%
e10013
8.8%
l7895
6.9%
s7594
6.6%
u7560
 
6.6%
r7532
 
6.6%
i6352
 
5.6%
c5901
 
5.2%
Other values (7)18967
16.6%
Space Separator
ValueCountFrequency (%)
1059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114283
99.1%
Common1059
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o15516
13.6%
a14006
12.3%
t12947
11.3%
e10013
8.8%
l7895
6.9%
s7594
6.6%
u7560
 
6.6%
r7532
 
6.6%
i6352
 
5.6%
c5901
 
5.2%
Other values (7)18967
16.6%
Common
ValueCountFrequency (%)
1059
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII115342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o15516
13.5%
a14006
12.1%
t12947
11.2%
e10013
8.7%
l7895
 
6.8%
s7594
 
6.6%
u7560
 
6.6%
r7532
 
6.5%
i6352
 
5.5%
c5901
 
5.1%
Other values (8)20026
17.4%

ClaseVehiculo__c
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size511.1 KiB
99999
8440 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters42200
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
999998440
100.0%

Length

2022-06-04T00:33:03.085506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.152006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
999998440
100.0%

Most occurring characters

ValueCountFrequency (%)
942200
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
942200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
942200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
942200
100.0%

MarcaVehiculo__c
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing8440
Missing (%)100.0%
Memory size66.1 KiB

MdeloVehiculo__c
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing8440
Missing (%)100.0%
Memory size66.1 KiB

TipoVehiculo__c
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size511.1 KiB
99999
8440 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters42200
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
999998440
100.0%

Length

2022-06-04T00:33:03.206506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.273006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
999998440
100.0%

Most occurring characters

ValueCountFrequency (%)
942200
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
942200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
942200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
942200
100.0%

NumeroPoliza__c
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8040
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201742.428
Minimum1000002
Maximum3173059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:03.455006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000002
5-th percentile1002345.7
Q11009391.75
median3007879.5
Q33068286.5
95-th percentile3166538.05
Maximum3173059
Range2173057
Interquartile range (IQR)2058894.75

Descriptive statistics

Standard deviation1015038.799
Coefficient of variation (CV)0.4610161414
Kurtosis-1.892128615
Mean2201742.428
Median Absolute Deviation (MAD)157102.5
Skewness-0.3157750033
Sum1.858270609 × 1010
Variance1.030303763 × 1012
MonotonicityNot monotonic
2022-06-04T00:33:03.541506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10061093
 
< 0.1%
10047473
 
< 0.1%
10056963
 
< 0.1%
10071493
 
< 0.1%
10061063
 
< 0.1%
10071643
 
< 0.1%
10042953
 
< 0.1%
10042673
 
< 0.1%
10071483
 
< 0.1%
30078963
 
< 0.1%
Other values (8030)8410
99.6%
ValueCountFrequency (%)
10000021
< 0.1%
10000041
< 0.1%
10000061
< 0.1%
10000091
< 0.1%
10000101
< 0.1%
10000141
< 0.1%
10000161
< 0.1%
10000171
< 0.1%
10000201
< 0.1%
10000221
< 0.1%
ValueCountFrequency (%)
31730591
< 0.1%
31730561
< 0.1%
31728551
< 0.1%
31728301
< 0.1%
31728031
< 0.1%
31719511
< 0.1%
31717901
< 0.1%
31707231
< 0.1%
31695121
< 0.1%
31695101
< 0.1%

FechaInicioVigencia__ctrim
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.6 KiB
02-2021
6628 
01-2021
1812 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters59080
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01-2021
2nd row01-2021
3rd row01-2021
4th row01-2021
5th row01-2021

Common Values

ValueCountFrequency (%)
02-20216628
78.5%
01-20211812
 
21.5%

Length

2022-06-04T00:33:03.619007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.687507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
02-20216628
78.5%
01-20211812
 
21.5%

Most occurring characters

ValueCountFrequency (%)
223508
39.8%
016880
28.6%
110252
17.4%
-8440
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50640
85.7%
Dash Punctuation8440
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
223508
46.4%
016880
33.3%
110252
20.2%
Dash Punctuation
ValueCountFrequency (%)
-8440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common59080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
223508
39.8%
016880
28.6%
110252
17.4%
-8440
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII59080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223508
39.8%
016880
28.6%
110252
17.4%
-8440
 
14.3%

churn
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size478.2 KiB
1
4453 
0
3987 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8440
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14453
52.8%
03987
47.2%

Length

2022-06-04T00:33:03.746006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.814506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
14453
52.8%
03987
47.2%

Most occurring characters

ValueCountFrequency (%)
14453
52.8%
03987
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8440
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14453
52.8%
03987
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common8440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14453
52.8%
03987
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14453
52.8%
03987
47.2%

n_prod_prev
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.2%
Missing6743
Missing (%)79.9%
Memory size363.0 KiB
2.0
1000 
8.0
474 
1.0
220 
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5091
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row1.0
4th row2.0
5th row8.0

Common Values

ValueCountFrequency (%)
2.01000
 
11.8%
8.0474
 
5.6%
1.0220
 
2.6%
3.03
 
< 0.1%
(Missing)6743
79.9%

Length

2022-06-04T00:33:03.873507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:03.946006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2.01000
58.9%
8.0474
27.9%
1.0220
 
13.0%
3.03
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.1697
33.3%
01697
33.3%
21000
19.6%
8474
 
9.3%
1220
 
4.3%
33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3394
66.7%
Other Punctuation1697
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01697
50.0%
21000
29.5%
8474
 
14.0%
1220
 
6.5%
33
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.1697
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5091
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.1697
33.3%
01697
33.3%
21000
19.6%
8474
 
9.3%
1220
 
4.3%
33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.1697
33.3%
01697
33.3%
21000
19.6%
8474
 
9.3%
1220
 
4.3%
33
 
0.1%

total_siniestros
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)1.1%
Missing6581
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean89.05648198
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.008506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q138
median150
Q3150
95-th percentile150
Maximum150
Range149
Interquartile range (IQR)112

Descriptive statistics

Standard deviation65.11927894
Coefficient of variation (CV)0.7312132423
Kurtosis-1.826563904
Mean89.05648198
Median Absolute Deviation (MAD)0
Skewness-0.202468804
Sum165556
Variance4240.520489
MonotonicityNot monotonic
2022-06-04T00:33:04.073006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
150974
 
11.5%
38474
 
5.6%
1227
 
2.7%
249
 
0.6%
341
 
0.5%
521
 
0.2%
415
 
0.2%
98
 
0.1%
78
 
0.1%
107
 
0.1%
Other values (11)35
 
0.4%
(Missing)6581
78.0%
ValueCountFrequency (%)
1227
2.7%
249
 
0.6%
341
 
0.5%
415
 
0.2%
521
 
0.2%
64
 
< 0.1%
78
 
0.1%
85
 
0.1%
98
 
0.1%
107
 
0.1%
ValueCountFrequency (%)
150974
11.5%
801
 
< 0.1%
522
 
< 0.1%
38474
5.6%
362
 
< 0.1%
282
 
< 0.1%
165
 
0.1%
145
 
0.1%
132
 
< 0.1%
124
 
< 0.1%

total_pagado_smmlv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct118
Distinct (%)6.3%
Missing6581
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean1478.300089
Minimum0
Maximum2414.537144
Zeros194
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.152006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1816.7201723
median2414.537144
Q32414.537144
95-th percentile2414.537144
Maximum2414.537144
Range2414.537144
Interquartile range (IQR)1597.816972

Descriptive statistics

Standard deviation1020.381999
Coefficient of variation (CV)0.6902400982
Kurtosis-1.656355747
Mean1478.300089
Median Absolute Deviation (MAD)0
Skewness-0.3116861175
Sum2748159.865
Variance1041179.423
MonotonicityNot monotonic
2022-06-04T00:33:04.239005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2414.537144974
 
11.5%
816.7201723474
 
5.6%
0194
 
2.3%
27.224583810
 
0.1%
25.860770586
 
0.1%
21.90361096
 
0.1%
1.4518693035
 
0.1%
190.3334255
 
0.1%
19.73886245
 
0.1%
18.201606744
 
< 0.1%
Other values (108)176
 
2.1%
(Missing)6581
78.0%
ValueCountFrequency (%)
0194
2.3%
0.075643396071
 
< 0.1%
0.19200196612
 
< 0.1%
0.2453135841
 
< 0.1%
0.32000327683
 
< 0.1%
0.418836251
 
< 0.1%
0.4302651051
 
< 0.1%
0.43278503262
 
< 0.1%
0.66666666671
 
< 0.1%
0.68305986651
 
< 0.1%
ValueCountFrequency (%)
2414.537144974
11.5%
877.13975462
 
< 0.1%
816.7201723474
5.6%
433.98868061
 
< 0.1%
248.38940181
 
< 0.1%
226.22456334
 
< 0.1%
190.3334255
 
0.1%
183.72708391
 
< 0.1%
175.90680072
 
< 0.1%
151.85190682
 
< 0.1%

anios_ultimo_siniestro
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct137
Distinct (%)7.4%
Missing6581
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean0.1627948448
Minimum0.002739726027
Maximum8.75890411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.327506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.002739726027
5-th percentile0.002739726027
Q10.002739726027
median0.002739726027
Q30.008219178082
95-th percentile0.8
Maximum8.75890411
Range8.756164384
Interquartile range (IQR)0.005479452055

Descriptive statistics

Standard deviation0.7025660932
Coefficient of variation (CV)4.315653202
Kurtosis55.58336597
Mean0.1627948448
Median Absolute Deviation (MAD)0
Skewness6.815919012
Sum302.6356164
Variance0.4935991153
MonotonicityNot monotonic
2022-06-04T00:33:04.410506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002739726027974
 
11.5%
0.008219178082481
 
5.7%
0.0767123287715
 
0.2%
0.0712328767113
 
0.2%
0.0438356164412
 
0.1%
0.0630136986311
 
0.1%
0.0136986301411
 
0.1%
0.811
 
0.1%
0.0739726027410
 
0.1%
0.010958904119
 
0.1%
Other values (127)312
 
3.7%
(Missing)6581
78.0%
ValueCountFrequency (%)
0.002739726027974
11.5%
0.0054794520559
 
0.1%
0.008219178082481
5.7%
0.010958904119
 
0.1%
0.0136986301411
 
0.1%
0.016438356163
 
< 0.1%
0.021917808225
 
0.1%
0.024657534255
 
0.1%
0.027397260271
 
< 0.1%
0.03013698633
 
< 0.1%
ValueCountFrequency (%)
8.758904111
 
< 0.1%
81
 
< 0.1%
7.1780821923
< 0.1%
6.2356164382
< 0.1%
6.0082191781
 
< 0.1%
5.8794520551
 
< 0.1%
5.0904109593
< 0.1%
4.3123287671
 
< 0.1%
4.172602741
 
< 0.1%
4.0438356164
< 0.1%

Activos__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1349
Distinct (%)32.4%
Missing4282
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean429678332.6
Minimum0
Maximum5.643561885 × 1010
Zeros22
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.495506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000000
Q146000000.25
median120000000
Q3320000000
95-th percentile1388556000
Maximum5.643561885 × 1010
Range5.643561885 × 1010
Interquartile range (IQR)273999999.8

Descriptive statistics

Standard deviation1757766913
Coefficient of variation (CV)4.090890277
Kurtosis356.5619219
Mean429678332.6
Median Absolute Deviation (MAD)99950000
Skewness15.89519263
Sum1.786602507 × 1012
Variance3.089744521 × 1018
MonotonicityNot monotonic
2022-06-04T00:33:04.580506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000129
 
1.5%
50000000118
 
1.4%
80000000107
 
1.3%
150000000101
 
1.2%
200000000100
 
1.2%
30000000089
 
1.1%
12000000087
 
1.0%
6000000086
 
1.0%
1000000084
 
1.0%
178
 
0.9%
Other values (1339)3179
37.7%
(Missing)4282
50.7%
ValueCountFrequency (%)
022
 
0.3%
178
0.9%
237
0.4%
203
 
< 0.1%
100001
 
< 0.1%
1072541
 
< 0.1%
1270001
 
< 0.1%
2000001
 
< 0.1%
5000004
 
< 0.1%
6000002
 
< 0.1%
ValueCountFrequency (%)
5.643561885 × 10101
 
< 0.1%
3.2934155 × 10102
 
< 0.1%
2.8207083 × 10101
 
< 0.1%
2.379921321 × 10101
 
< 0.1%
1.9483034 × 10103
< 0.1%
1.7267744 × 10107
0.1%
1.56067031 × 10105
0.1%
1.1530782 × 10101
 
< 0.1%
1.024294018 × 10102
 
< 0.1%
76945340001
 
< 0.1%

AnnualRevenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1454
Distinct (%)35.0%
Missing4282
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean320064858.7
Minimum0
Maximum4.24173603 × 1010
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.670506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9039950
Q124000000
median45736000
Q398000000
95-th percentile777008000
Maximum4.24173603 × 1010
Range4.24173603 × 1010
Interquartile range (IQR)74000000

Descriptive statistics

Standard deviation2047397773
Coefficient of variation (CV)6.396821511
Kurtosis202.3120142
Mean320064858.7
Median Absolute Deviation (MAD)26264000
Skewness13.33129356
Sum1.330829682 × 1012
Variance4.191837641 × 1018
MonotonicityNot monotonic
2022-06-04T00:33:04.756006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000000170
 
2.0%
24000000139
 
1.6%
60000000126
 
1.5%
30000000125
 
1.5%
48000000117
 
1.4%
1200000091
 
1.1%
4000000078
 
0.9%
5000000068
 
0.8%
1800000067
 
0.8%
4200000052
 
0.6%
Other values (1444)3125
37.0%
(Missing)4282
50.7%
ValueCountFrequency (%)
01
 
< 0.1%
18
0.1%
522301
 
< 0.1%
907901
 
< 0.1%
8350001
 
< 0.1%
8770001
 
< 0.1%
9000001
 
< 0.1%
9100001
 
< 0.1%
100000012
0.1%
11000004
 
< 0.1%
ValueCountFrequency (%)
4.24173603 × 10102
 
< 0.1%
3.243391822 × 10101
 
< 0.1%
2.9375273 × 10107
0.1%
2.765797 × 10103
< 0.1%
1.897052621 × 10105
0.1%
1.7620986 × 10101
 
< 0.1%
1.742471143 × 10101
 
< 0.1%
1.4350938 × 10103
< 0.1%
1.2306478 × 10101
 
< 0.1%
1.0812427 × 10101
 
< 0.1%

MontoAnual__c
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)57.1%
Missing8433
Missing (%)99.9%
Memory size330.0 KiB
0.0
102.0
3000.0
100.0

Length

Max length6
Median length3
Mean length4
Min length3

Characters and Unicode

Total characters28
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)42.9%

Sample

1st row102.0
2nd row3000.0
3rd row0.0
4th row0.0
5th row100.0

Common Values

ValueCountFrequency (%)
0.04
 
< 0.1%
102.01
 
< 0.1%
3000.01
 
< 0.1%
100.01
 
< 0.1%
(Missing)8433
99.9%

Length

2022-06-04T00:33:04.838506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:04.912506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04
57.1%
102.01
 
14.3%
3000.01
 
14.3%
100.01
 
14.3%

Most occurring characters

ValueCountFrequency (%)
017
60.7%
.7
25.0%
12
 
7.1%
21
 
3.6%
31
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21
75.0%
Other Punctuation7
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017
81.0%
12
 
9.5%
21
 
4.8%
31
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017
60.7%
.7
25.0%
12
 
7.1%
21
 
3.6%
31
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017
60.7%
.7
25.0%
12
 
7.1%
21
 
3.6%
31
 
3.6%

OtrosIngresos__c
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct105
Distinct (%)3.1%
Missing5105
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean2825766.49
Minimum0
Maximum3479487967
Zeros3168
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:04.986506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum3479487967
Range3479487967
Interquartile range (IQR)0

Descriptive statistics

Standard deviation62803920.5
Coefficient of variation (CV)22.22544599
Kurtosis2823.733557
Mean2825766.49
Median Absolute Deviation (MAD)0
Skewness51.4657287
Sum9423931245
Variance3.944332431 × 1015
MonotonicityNot monotonic
2022-06-04T00:33:05.193007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03168
37.5%
120000007
 
0.1%
200000007
 
0.1%
240000006
 
0.1%
50000006
 
0.1%
20000005
 
0.1%
60000005
 
0.1%
241997855
 
0.1%
150000004
 
< 0.1%
360000004
 
< 0.1%
Other values (95)118
 
1.4%
(Missing)5105
60.5%
ValueCountFrequency (%)
03168
37.5%
22
 
< 0.1%
40001
 
< 0.1%
90001
 
< 0.1%
370001
 
< 0.1%
2270002
 
< 0.1%
2740001
 
< 0.1%
5290001
 
< 0.1%
5290831
 
< 0.1%
5777741
 
< 0.1%
ValueCountFrequency (%)
34794879671
< 0.1%
6554900001
< 0.1%
5179690001
< 0.1%
1962181201
< 0.1%
1879690001
< 0.1%
1656440002
< 0.1%
1584000001
< 0.1%
1440000001
< 0.1%
1152800002
< 0.1%
1098560001
< 0.1%

Profesion__pc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing8440
Missing (%)100.0%
Memory size66.1 KiB

EgresosAnuales__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1096
Distinct (%)26.4%
Missing4282
Missing (%)50.7%
Infinite0
Infinite (%)0.0%
Mean271922356.5
Minimum0
Maximum4.033890964 × 1010
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:05.286506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3485000
Q112680250
median26255000
Q360000000
95-th percentile600492469.5
Maximum4.033890964 × 1010
Range4.033890964 × 1010
Interquartile range (IQR)47319750

Descriptive statistics

Standard deviation1982687195
Coefficient of variation (CV)7.291372512
Kurtosis213.5733155
Mean271922356.5
Median Absolute Deviation (MAD)16255000
Skewness13.83240589
Sum1.130653158 × 1012
Variance3.931048514 × 1018
MonotonicityNot monotonic
2022-06-04T00:33:05.377507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000000183
 
2.2%
30000000176
 
2.1%
18000000156
 
1.8%
24000000155
 
1.8%
20000000134
 
1.6%
36000000100
 
1.2%
40000000100
 
1.2%
10000000100
 
1.2%
1500000090
 
1.1%
2500000078
 
0.9%
Other values (1086)2886
34.2%
(Missing)4282
50.7%
ValueCountFrequency (%)
02
 
< 0.1%
125
0.3%
3000001
 
< 0.1%
3395611
 
< 0.1%
4000002
 
< 0.1%
4500007
 
0.1%
4973951
 
< 0.1%
5000009
 
0.1%
5800001
 
< 0.1%
6000005
 
0.1%
ValueCountFrequency (%)
4.033890964 × 10102
 
< 0.1%
3.472297457 × 10101
 
< 0.1%
3.36 × 10101
 
< 0.1%
2.8430656 × 10107
0.1%
2.5171626 × 10103
< 0.1%
1.663542953 × 10101
 
< 0.1%
1.6135595 × 10101
 
< 0.1%
1.530540134 × 10105
0.1%
1.4149066 × 10103
< 0.1%
1.0675292 × 10101
 
< 0.1%

EstadoCivil__pc
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.2%
Missing4141
Missing (%)49.1%
Memory size394.9 KiB
SOLTERO
2329 
CASADO
1203 
OTRO
689 
UNIDO
 
49
VIUDO
 
12
Other values (3)
 
17

Length

Max length10
Median length7
Mean length6.217027216
Min length3

Characters and Unicode

Total characters26727
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSOLTERO
2nd rowSOLTERO
3rd rowSOLTERO
4th rowCASADO
5th rowSOLTERO

Common Values

ValueCountFrequency (%)
SOLTERO2329
27.6%
CASADO1203
 
14.3%
OTRO689
 
8.2%
UNIDO49
 
0.6%
VIUDO12
 
0.1%
SEPARADO9
 
0.1%
DIVORCIADO7
 
0.1%
N A1
 
< 0.1%
(Missing)4141
49.1%

Length

2022-06-04T00:33:05.464006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:05.546007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
soltero2329
54.2%
casado1203
28.0%
otro689
 
16.0%
unido49
 
1.1%
viudo12
 
0.3%
separado9
 
0.2%
divorciado7
 
0.2%
n1
 
< 0.1%
a1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O7323
27.4%
S3541
13.2%
R3034
11.4%
T3018
11.3%
A2432
 
9.1%
E2338
 
8.7%
L2329
 
8.7%
D1287
 
4.8%
C1210
 
4.5%
I75
 
0.3%
Other values (5)140
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter26726
> 99.9%
Space Separator1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O7323
27.4%
S3541
13.2%
R3034
11.4%
T3018
11.3%
A2432
 
9.1%
E2338
 
8.7%
L2329
 
8.7%
D1287
 
4.8%
C1210
 
4.5%
I75
 
0.3%
Other values (4)139
 
0.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26726
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O7323
27.4%
S3541
13.2%
R3034
11.4%
T3018
11.3%
A2432
 
9.1%
E2338
 
8.7%
L2329
 
8.7%
D1287
 
4.8%
C1210
 
4.5%
I75
 
0.3%
Other values (4)139
 
0.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O7323
27.4%
S3541
13.2%
R3034
11.4%
T3018
11.3%
A2432
 
9.1%
E2338
 
8.7%
L2329
 
8.7%
D1287
 
4.8%
C1210
 
4.5%
I75
 
0.3%
Other values (5)140
 
0.5%

Genero__pc
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing4141
Missing (%)49.1%
Memory size405.5 KiB
MASCULINO
3122 
FEMENINO
1176 
N A
 
1

Length

Max length9
Median length9
Mean length8.725052338
Min length3

Characters and Unicode

Total characters37509
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMASCULINO
2nd rowFEMENINO
3rd rowMASCULINO
4th rowFEMENINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
MASCULINO3122
37.0%
FEMENINO1176
 
13.9%
N A1
 
< 0.1%
(Missing)4141
49.1%

Length

2022-06-04T00:33:05.623008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-04T00:33:05.692508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
masculino3122
72.6%
femenino1176
 
27.3%
n1
 
< 0.1%
a1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N5475
14.6%
M4298
11.5%
I4298
11.5%
O4298
11.5%
A3123
8.3%
S3122
8.3%
C3122
8.3%
U3122
8.3%
L3122
8.3%
E2352
6.3%
Other values (2)1177
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter37508
> 99.9%
Space Separator1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N5475
14.6%
M4298
11.5%
I4298
11.5%
O4298
11.5%
A3123
8.3%
S3122
8.3%
C3122
8.3%
U3122
8.3%
L3122
8.3%
E2352
6.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin37508
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N5475
14.6%
M4298
11.5%
I4298
11.5%
O4298
11.5%
A3123
8.3%
S3122
8.3%
C3122
8.3%
U3122
8.3%
L3122
8.3%
E2352
6.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII37509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N5475
14.6%
M4298
11.5%
I4298
11.5%
O4298
11.5%
A3123
8.3%
S3122
8.3%
C3122
8.3%
U3122
8.3%
L3122
8.3%
E2352
6.3%
Other values (2)1177
 
3.1%

ciudad_name
Categorical

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)0.5%
Missing4141
Missing (%)49.1%
Memory size397.0 KiB
otras
3769 
BOGOTÁ D.C.
 
185
MEDELLIN
 
80
CALI
 
39
PASTO
 
26
Other values (17)
 
200

Length

Max length13
Median length5
Mean length5.478250756
Min length4

Characters and Unicode

Total characters23551
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowotras
2nd rowotras
3rd rowotras
4th rowotras
5th rowotras

Common Values

ValueCountFrequency (%)
otras3769
44.7%
BOGOTÁ D.C.185
 
2.2%
MEDELLIN80
 
0.9%
CALI39
 
0.5%
PASTO26
 
0.3%
BUCARAMANGA25
 
0.3%
ARMENIA25
 
0.3%
MANIZALES24
 
0.3%
CÚCUTA23
 
0.3%
BARRANQUILLA19
 
0.2%
Other values (12)84
 
1.0%
(Missing)4141
49.1%

Length

2022-06-04T00:33:05.758506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
otras3769
83.8%
bogotá185
 
4.1%
d.c185
 
4.1%
medellin80
 
1.8%
cali39
 
0.9%
pasto26
 
0.6%
bucaramanga25
 
0.6%
armenia25
 
0.6%
manizales24
 
0.5%
cúcuta23
 
0.5%
Other values (17)119
 
2.6%

Most occurring characters

ValueCountFrequency (%)
o3769
16.0%
r3769
16.0%
a3769
16.0%
s3769
16.0%
t3769
16.0%
A457
 
1.9%
O433
 
1.8%
.370
 
1.6%
C341
 
1.4%
L300
 
1.3%
Other values (21)2805
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18845
80.0%
Uppercase Letter4135
 
17.6%
Other Punctuation370
 
1.6%
Space Separator201
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A457
11.1%
O433
10.5%
C341
 
8.2%
L300
 
7.3%
E294
 
7.1%
D281
 
6.8%
I278
 
6.7%
T248
 
6.0%
B232
 
5.6%
N230
 
5.6%
Other values (14)1041
25.2%
Lowercase Letter
ValueCountFrequency (%)
o3769
20.0%
r3769
20.0%
a3769
20.0%
s3769
20.0%
t3769
20.0%
Other Punctuation
ValueCountFrequency (%)
.370
100.0%
Space Separator
ValueCountFrequency (%)
201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22980
97.6%
Common571
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3769
16.4%
r3769
16.4%
a3769
16.4%
s3769
16.4%
t3769
16.4%
A457
 
2.0%
O433
 
1.9%
C341
 
1.5%
L300
 
1.3%
E294
 
1.3%
Other values (19)2310
10.1%
Common
ValueCountFrequency (%)
.370
64.8%
201
35.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII23332
99.1%
None219
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3769
16.2%
r3769
16.2%
a3769
16.2%
s3769
16.2%
t3769
16.2%
A457
 
2.0%
O433
 
1.9%
.370
 
1.6%
C341
 
1.5%
L300
 
1.3%
Other values (18)2586
11.1%
None
ValueCountFrequency (%)
Á194
88.6%
Ú23
 
10.5%
É2
 
0.9%

edad
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3487
Distinct (%)81.1%
Missing4142
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean46.44473186
Minimum1.578082192
Maximum122.5041096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.1 KiB
2022-06-04T00:33:05.835006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.578082192
5-th percentile26.20808219
Q135.35410959
median45.00273973
Q356.35410959
95-th percentile70.82684932
Maximum122.5041096
Range120.9260274
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.21402064
Coefficient of variation (CV)0.3060416126
Kurtosis0.1622937524
Mean46.44473186
Median Absolute Deviation (MAD)10.44931507
Skewness0.4328453742
Sum199619.4575
Variance202.0383827
MonotonicityNot monotonic
2022-06-04T00:33:05.923507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.802739738
 
0.1%
58.145205487
 
0.1%
73.043835627
 
0.1%
51.915068496
 
0.1%
64.909589046
 
0.1%
35.578082195
 
0.1%
61.602739735
 
0.1%
48.046575345
 
0.1%
42.452054795
 
0.1%
76.287671235
 
0.1%
Other values (3477)4239
50.2%
(Missing)4142
49.1%
ValueCountFrequency (%)
1.5780821921
< 0.1%
2.4027397261
< 0.1%
5.613698631
< 0.1%
6.3561643841
< 0.1%
7.5808219182
< 0.1%
7.8465753422
< 0.1%
8.6383561641
< 0.1%
8.986301371
< 0.1%
9.9205479451
< 0.1%
10.956164381
< 0.1%
ValueCountFrequency (%)
122.50410963
< 0.1%
112.79178081
 
< 0.1%
103.06575341
 
< 0.1%
89.61095891
 
< 0.1%
88.86301371
 
< 0.1%
88.484931511
 
< 0.1%
87.542465751
 
< 0.1%
87.443835622
< 0.1%
86.52602741
 
< 0.1%
85.865753421
 
< 0.1%

Interactions

2022-06-04T00:32:56.622506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:48.293506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:58.907506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:31.818009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.315506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.748006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:39.211506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.512506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:48.027508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:52.010507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:57.485506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:49.570508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:04.617506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:32.214006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.859008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:37.136010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:40.074506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:44.357506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:48.873506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:52.882006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:00.595006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:55.681507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:15.403505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:33.718008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.207506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.520507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:42.929006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.449506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.385506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:55.992006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:00.661008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:55.894006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:16.444507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:33.800506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.287007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.598008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:42.998506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.519506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.461506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.063506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:00.720508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:56.103507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:18.434006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:33.879506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.361011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.671506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.061007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.581506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.526506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.125506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:01.035507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:56.311506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:19.634006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:33.954507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.434006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.742006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.129006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.647512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.598006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.194506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:01.110506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:56.727006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:22.060008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.034508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.498006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.808007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.204006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.720506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.676509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.276006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:01.181508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:57.141006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:24.686506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.105008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.559507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.871508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.276006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.791506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.760007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.355006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:01.262506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:57.555507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:26.687007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.180508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.626505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:38.941006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.355007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.872508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.844007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.439006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:33:01.344008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:31:57.976511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:29.246506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:34.248506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:36.687508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:39.149006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:43.436009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:47.953008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:51.928008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-04T00:32:56.527007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-04T00:33:06.002007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-04T00:33:06.135506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-04T00:33:06.266007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-04T00:33:06.403005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-04T00:33:01.498506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-04T00:33:01.947506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-04T00:33:02.155506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-04T00:33:02.334507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CodigoTipoAsegurado__cPuntoVenta__ctipo_ramo_nametipo_prod_descClaseVehiculo__cMarcaVehiculo__cMdeloVehiculo__cTipoVehiculo__cNumeroPoliza__cFechaInicioVigencia__ctrimchurnn_prod_prevtotal_siniestrostotal_pagado_smmlvanios_ultimo_siniestroActivos__cAnnualRevenueMontoAnual__cOtrosIngresos__cProfesion__pcEgresosAnuales__cEstadoCivil__pcGenero__pcciudad_nameedad
011203responsabilidad civilextracontractual99999NaNNaN99999100992301-20210NaNNaNNaNNaN500000000.0300000000.0NaNNaNNaN250000000.0SOLTEROMASCULINOotras34.846575
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211002automovilesautomoviles99999NaNNaN99999305243201-202108.038.0816.7201720.008219NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
318042automovilesautomoviles99999NaNNaN99999307555901-20211NaNNaNNaNNaN80000000.040000000.0NaN0.0NaN14000000.0SOLTEROFEMENINOotras40.619178
441048responsabilidad civilextracontractual99999NaNNaN99999300118501-20210NaN1.00.0000001.087671NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51604responsabilidad civilextracontractual99999NaNNaN99999101015001-20210NaNNaNNaNNaN77000000.040000000.0NaN0.0NaN27000000.0SOLTEROMASCULINOotras62.473973
613202responsabilidad civilextracontractual99999NaNNaN99999105855801-20210NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72404responsabilidad civilextracontractual99999NaNNaN99999100201301-20210NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
84404responsabilidad civilextracontractual99999NaNNaN99999100116901-20210NaN2.00.0000001.452055NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
911048responsabilidad civilprofesionales medicos99999NaNNaN99999100426001-20211NaN1.04.9714630.136986NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

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843015responsabilidad civilextracontractual99999NaNNaN99999100006002-20211NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843119477automovilesautomoviles99999NaNNaN99999305177602-202108.038.0816.7201720.008219NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843219721automovilesautomoviles99999NaNNaN99999317305602-202112.0150.02414.5371440.002740NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
84331604automovilesautomoviles99999NaNNaN99999300779202-20211NaNNaNNaNNaN100000000.053000000.0NaN0.0NaN47000000.0UNIDOMASCULINOotras37.139726
843411701automovilesautomoviles99999NaNNaN99999300023802-202111.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843518001responsabilidad civilprofesionales medicos99999NaNNaN99999100421502-20211NaN16.0190.3334250.060274NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843613301automovilesautomoviles99999NaNNaN99999307577302-20211NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843718001responsabilidad civilextracontractual99999NaNNaN99999102725502-20211NaNNaNNaNNaN600000000.0100000000.0NaN0.0NaN70000000.0SOLTEROMASCULINOBOGOTÁ D.C.51.517808
843818001responsabilidad civilextracontractual99999NaNNaN99999102733102-20210NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
843918001responsabilidad civilextracontractual99999NaNNaN99999102742802-20211NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN